用途
ngroup：
对于新列
index
df[‘index’] = df.groupby(‘date’, sort=False).ngroup() + 1
print (df)
date price neg_vol pos_vol index
0 2017-10-17 01:00:00 51.88 11 4 1
1 2017-10-17 01:00:00 51.89 10 2 1
2 2017-10-17 01:00:00 51.90 16 27 1
3 2017-10-17 01:00:00 51.91 1 10 1
4 2017-10-17 01:05:00 51.87 12 0 2
5 2017-10-17 01:05:00 51.88 0 12 2
6 2017-10-17 01:10:00 51.87 8 0 3
7 2017-10-17 01:10:00 51.88 0 5 3
8 2017-10-17 01:15:00 51.87 12 0 4
9 2017-10-17 01:15:00 51.88 0 8 4
10 2017-10-17 01:20:00 51.87 6 0 5用于新索引
df.index = df.groupby(‘date’, sort=False).ngroup() + 1
print (df)
date price neg_vol pos_vol
1 2017-10-17 01:00:00 51.88 11 4
1 2017-10-17 01:00:00 51.89 10 2
1 2017-10-17 01:00:00 51.90 16 27
1 2017-10-17 01:00:00 51.91 1 10
2 2017-10-17 01:05:00 51.87 12 0
2 2017-10-17 01:05:00 51.88 0 12
3 2017-10-17 01:10:00 51.87 8 0
3 2017-10-17 01:10:00 51.88 0 5
4 2017-10-17 01:15:00 51.87 12 0
4 2017-10-17 01:15:00 51.88 0 8
5 2017-10-17 01:20:00 51.87 6 0
另一个解决方案是
factorize：
df['index'] = pd.factorize(df['date'])[0] + 1
df.index = pd.factorize(df['date'])[0] + 1
